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He just happens to prefer Taco shaped diagrams.

US states have a minimum threshold of $100,000 and upwards so you can be compliant without having to worry about it until you start making larger revenue. Most startups never reach these generous thresholds in the first place.

> Nine months after our public launch in 2021, we surpassed $1M in ARR and never looked back.

Stripe is buying a lucrative business, but more importantly it is buying the complex knowhow of running an international tax-compliant merchant of record. They can then integrate this into their product. I switched to LemonSqueezy primarily out of concern for EU-wide tax ramifications (EU strikes again). Is there a reason to choose LemonSqueezy over Stripe if you are located in the US? There hasn't been a single VAT-compliance case to my knowledge and the need for MoR is unclear.

Have been freelancing for ages and in my experience talking about recent books you have read that are relevant to the employment absolutely seals the deal. Implicitly it shows you are 10 out of 10 on a topic.

Surprised to see Redux featured so prominent in JS frameworks section, since it is so often criticized while many praise newer competitors like Zustand.

As a European, after decades of regulations and fines without much to show, nobody in the industry believes the EU the capable of creating tech ecosystem. Perhaps even that the EU is part of the problem and that individual countries could independently move much faster.

> Dotenv is only good for local dev.

It wouldn't surprise me if many VPS use .env files.

The confusion also stems from companies deliberately using the term for marketing purposes. Strong separation between the open source, academia and business environments doesn’t exist as much as it used to. Can’t imagine a Richard Stallman or Linus Thorvalds engaging in blurry half-open source, half business projects.

> Ultra-low—and indeed negative—prices suggest that it is not being put to good use at present, reflecting failures in both infrastructure and regulation.

The fundamental issue is that you can't control when and how much solar or wind energy to produce and it can't be stored at anywhere the scale needed.

Had to Google it since the post didn't link to either, but I already knew Rust and assumed many of the readers already do. Also haven't had my morning coffee and thus slight irritated :). Relevant linking should be basic part of any post.

Zig language mentioned in the post: https://ziglang.org/

> I know from experience that you get a lot smarter if you don’t work every waking hour of the day, and any project is realistically better off run by a refreshed smart guy than an overworked idiot that hasn’t seen daylight for a week.

Good ideas are born in your free time when you put the daily work away and are able to zoom out. Returning Monday after a (long) weekend with a pair of new glasses can be profoundly productive.

If you want performant styling stick to CSS.

Apple should fund some of the most popular open source tools used on their Macs. They have the funds, its good marketing, it makes business sense. Nothing speaks against it.

Growing up on a farm improves the immunological responses that regulates allergies, autoimmune and inflammatory diseases. Although it isn't exactly clear what causes it:

> The researchers suggest additional work is required to determine the extent to which other farm-associated factors, such as social and maternal interactions, aerial contaminants, antigens from bedding and early nutrition, contributed to the impact of the environment on increased local and systemic immune regulation. [0]

[0]: https://www.sciencedaily.com/releases/2012/02/120208132549.h...

Congratulations, 10 years is an impressive effort. With regards to the AI capabilities, there is a lot more opportunity hidden besides text-to-SQL generation like query validation, fixing, optimziation, data generation etc. Perhaps these generators can inspire you: https://www.sqlai.ai/app

> Thus, you may expect that effective prompting for Text-to-SQL should include structured schema definitions; indeed.

I found that the simpler the better, when testing lots of different SQL schema formats on https://www.sqlai.ai/. CSV (table name, table column, data type) outperformed both a JSON formatted and SQL schema dump. And not to mention consumed fewer tokens.

If you need the database schema in a consistent format (e.g. CSV) just have LLM extract data and convert whatever the user provides into CSV. It shines at this.

S3 does handle authentication by giving you a temporary upload URL so your bucket isn't wide open. But I agree it isn't the simplest solution.

Difficult to quantify but as an example the 2017 scientific paper “Attention is all you need” changed the entire AI field dramatically. Without these landmark achievements delivered by highly skilled scientists, OpenAI wouldn’t exist or only be severely limited.

How much power does their microreactors generate?

Are there any open source implementation of neural network "functions"? And the layering, transformers and attention mechanisms.

One thing is the detrimental effects of general usage, but what happens to children when they encounter “extreme” content?

I wouldn’t be surprised if the numbers are similar in the rest of Europe and North America. Even national deviations, the numbers are high.

I cancelled my subscription mainly because it made me neglect overall structure and instead autocomplete quick inline and repetitive solutions. Haven’t missed it.

Database hosting is often the costly and complex part of hosting. I have played with the idea before of setting up a Hetzner VPS with 2CPU/8GB RAM and harden the it and setting up backups and then use it for all my databases. I don't have performance/scalability concerns, but I would be concerned with reliability and potentially security.

> Monthly fees for users building on Increase vary by use case.

I am currently adding public API access to AI-powered text-to-SQL endpoint with RAG support and the my biggest issue is the pricing. Anybody have a ballpark figure what we could be talking about here? Pricing must account for OpenAI tokens (or perhaps letting them add their own OpenAI token), database usage and likely caching/rate limiting setup down the line.

This can be optimized both easier and further[0]. How is case-sensitivity optimally handled? Should the query only match exact titles?

[0]: https://www.sqlai.ai/snippets/cluzdmi8w006d53gt82mguaga

Heard a podcast with one of Germany leading nutritionist advocating for banning energy drinks completely. Sugary beverages targeting teenagers filled with chemicals and caffeine and sold in large containers with zero health benefits – "why are they even allowed?" he asked rhetorically (paraphrasing).

You could ask the same question for any of the nova class 4 products[0].

[0]: https://world.openfoodfacts.org/nova

Edit: This study suggest connection between colon cancer and sugary beverages:

> Conclusion Higher SSB (Sugar-sweetened beverage) intake in adulthood and adolescence was associated with a higher risk of EO-CRC (early-onset colorectal cancer) among women. Reduction of SSB consumption among adolescents and young adults may serve as a potential strategy to alleviate the growing burden of EO-CRC.

https://gut.bmj.com/content/70/12/2330

Its TailwindCSS. Classes are CSS, can’t really match those.

> How does it handle large schemas?

For now we don't particularly handle huge schema, being over 10-20k tokens. I would suggest adding only the tables you need. But I will hopefully this week test a solution that automatically pre-determines which tables to include for huge schemas before the actual SQL query generation and thereby using relatively few tokens.

> How easy is it to select which tables should be taken into account?

You can edit the schema using code editor (it will be in CSV). That is relatively easy. I previously had a solution where you could manually tick off tables to include, but found it a bit cumbersome. Might add that back though. Ideally I want everything you run without the user having to include/exclude tables.

> Is there an intermediate context layer step to work with this?

I will test adding this. Must also be performant in terms of speed and reliability.

> Do you need to provide working examples for the tool to know how joins and relations are usually handled?

Normally AI can infer that from the schema. If not you can "teach" AI using RAG: https://www.sqlai.ai/posts/enhancing-ai-accuracy-for-sql-gen...